package beaver.game;

import java.util.Random;

//JAVA REFERENCE IMPLEMENTATION OF IMPROVED NOISE - COPYRIGHT 2002 KEN PERLIN.

public class Noise {  // Simplex noise in 2D, 3D and 4D
	private static int grad3[][] = {{1,1,0},{-1,1,0},{1,-1,0},{-1,-1,0},
		{1,0,1},{-1,0,1},{1,0,-1},{-1,0,-1},
		{0,1,1},{0,-1,1},{0,1,-1},{0,-1,-1}};
	private static int grad4[][]= {{0,1,1,1}, {0,1,1,-1}, {0,1,-1,1}, {0,1,-1,-1},
		{0,-1,1,1}, {0,-1,1,-1}, {0,-1,-1,1}, {0,-1,-1,-1},
		{1,0,1,1}, {1,0,1,-1}, {1,0,-1,1}, {1,0,-1,-1},
		{-1,0,1,1}, {-1,0,1,-1}, {-1,0,-1,1}, {-1,0,-1,-1},
		{1,1,0,1}, {1,1,0,-1}, {1,-1,0,1}, {1,-1,0,-1},
		{-1,1,0,1}, {-1,1,0,-1}, {-1,-1,0,1}, {-1,-1,0,-1},
		{1,1,1,0}, {1,1,-1,0}, {1,-1,1,0}, {1,-1,-1,0},
		{-1,1,1,0}, {-1,1,-1,0}, {-1,-1,1,0}, {-1,-1,-1,0}};
	private static int p[] = {151,160,137,91,90,15,
		131,13,201,95,96,53,194,233,7,225,140,36,103,30,69,142,8,99,37,240,21,10,23,
		190, 6,148,247,120,234,75,0,26,197,62,94,252,219,203,117,35,11,32,57,177,33,
		88,237,149,56,87,174,20,125,136,171,168, 68,175,74,165,71,134,139,48,27,166,
		77,146,158,231,83,111,229,122,60,211,133,230,220,105,92,41,55,46,245,40,244,
		102,143,54, 65,25,63,161, 1,216,80,73,209,76,132,187,208, 89,18,169,200,196,
		135,130,116,188,159,86,164,100,109,198,173,186, 3,64,52,217,226,250,124,123,
		5,202,38,147,118,126,255,82,85,212,207,206,59,227,47,16,58,17,182,189,28,42,
		223,183,170,213,119,248,152, 2,44,154,163, 70,221,153,101,155,167, 43,172,9,
		129,22,39,253, 19,98,108,110,79,113,224,232,178,185, 112,104,218,246,97,228,
		251,34,242,193,238,210,144,12,191,179,162,241, 81,51,145,235,249,14,239,107,
		49,192,214, 31,181,199,106,157,184, 84,204,176,115,121,50,45,127, 4,150,254,
		138,236,205,93,222,114,67,29,24,72,243,141,128,195,78,66,215,61,156,180};
	// To remove the need for index wrapping, double the permutation table length
	private static int perm[] = new int[512];
	static { for(int i=0; i<512; i++) perm[i]=p[i & 255]; }
	// A lookup table to traverse the simplex around a given point in 4D.
	// Details can be found where this table is used, in the 4D noise method.
	private static int simplex[][] = {
		{0,1,2,3},{0,1,3,2},{0,0,0,0},{0,2,3,1},{0,0,0,0},{0,0,0,0},{0,0,0,0},{1,2,3,0},
		{0,2,1,3},{0,0,0,0},{0,3,1,2},{0,3,2,1},{0,0,0,0},{0,0,0,0},{0,0,0,0},{1,3,2,0},
		{0,0,0,0},{0,0,0,0},{0,0,0,0},{0,0,0,0},{0,0,0,0},{0,0,0,0},{0,0,0,0},{0,0,0,0},
		{1,2,0,3},{0,0,0,0},{1,3,0,2},{0,0,0,0},{0,0,0,0},{0,0,0,0},{2,3,0,1},{2,3,1,0},
		{1,0,2,3},{1,0,3,2},{0,0,0,0},{0,0,0,0},{0,0,0,0},{2,0,3,1},{0,0,0,0},{2,1,3,0},
		{0,0,0,0},{0,0,0,0},{0,0,0,0},{0,0,0,0},{0,0,0,0},{0,0,0,0},{0,0,0,0},{0,0,0,0},
		{2,0,1,3},{0,0,0,0},{0,0,0,0},{0,0,0,0},{3,0,1,2},{3,0,2,1},{0,0,0,0},{3,1,2,0},
		{2,1,0,3},{0,0,0,0},{0,0,0,0},{0,0,0,0},{3,1,0,2},{0,0,0,0},{3,2,0,1},{3,2,1,0}};
	// This method is a *lot* faster than using (int)Math.floor(x)
	private static int fastfloor(double x) {
		return x>0 ? (int)x : (int)x-1;
	}
	private static double dot(int g[], double x, double y) {
		return g[0]*x + g[1]*y; }
	private static double dot(int g[], double x, double y, double z) {
		return g[0]*x + g[1]*y + g[2]*z; }
	private static double dot(int g[], double x, double y, double z, double w) {
		return g[0]*x + g[1]*y + g[2]*z + g[3]*w; }  // 2D simplex noise
	public static double noise(double xin, double yin) {
		double n0, n1, n2; // Noise contributions from the three corners
		// Skew the input space to determine which simplex cell we're in
		final double F2 = 0.5*(Math.sqrt(3.0)-1.0);
		double s = (xin+yin)*F2; // Hairy factor for 2D
		int i = fastfloor(xin+s);
		int j = fastfloor(yin+s);
		final double G2 = (3.0-Math.sqrt(3.0))/6.0;
		double t = (i+j)*G2;
		double X0 = i-t; // Unskew the cell origin back to (x,y) space
		double Y0 = j-t;
		double x0 = xin-X0; // The x,y distances from the cell origin
		double y0 = yin-Y0;
		// For the 2D case, the simplex shape is an equilateral triangle.
		// Determine which simplex we are in.
		int i1, j1; // Offsets for second (middle) corner of simplex in (i,j) coords
		if(x0>y0) {i1=1; j1=0;} // lower triangle, XY order: (0,0)->(1,0)->(1,1)
		else {i1=0; j1=1;}      // upper triangle, YX order: (0,0)->(0,1)->(1,1)
		// A step of (1,0) in (i,j) means a step of (1-c,-c) in (x,y), and
		// a step of (0,1) in (i,j) means a step of (-c,1-c) in (x,y), where
		// c = (3-sqrt(3))/6
		double x1 = x0 - i1 + G2; // Offsets for middle corner in (x,y) unskewed coords
		double y1 = y0 - j1 + G2;
		double x2 = x0 - 1.0 + 2.0 * G2; // Offsets for last corner in (x,y) unskewed coords
		double y2 = y0 - 1.0 + 2.0 * G2;
		// Work out the hashed gradient indices of the three simplex corners
		int ii = i & 255;
		int jj = j & 255;
		int gi0 = perm[ii+perm[jj]] % 12;
		int gi1 = perm[ii+i1+perm[jj+j1]] % 12;
		int gi2 = perm[ii+1+perm[jj+1]] % 12;
		// Calculate the contribution from the three corners
		double t0 = 0.5 - x0*x0-y0*y0;
		if(t0<0) n0 = 0.0;
		else {
			t0 *= t0;
			n0 = t0 * t0 * dot(grad3[gi0], x0, y0);  // (x,y) of grad3 used for 2D gradient
		}
		double t1 = 0.5 - x1*x1-y1*y1;
		if(t1<0) n1 = 0.0;
		else {
			t1 *= t1;
			n1 = t1 * t1 * dot(grad3[gi1], x1, y1);
		}    double t2 = 0.5 - x2*x2-y2*y2;
		if(t2<0) n2 = 0.0;
		else {
			t2 *= t2;
			n2 = t2 * t2 * dot(grad3[gi2], x2, y2);
		}
		// Add contributions from each corner to get the final noise value.
		// The result is scaled to return values in the interval [-1,1].
		return 70.0 * (n0 + n1 + n2);
	}

	
	static float[][] GenerateWhiteNoise(int width, int height)
	{
	    Random random = new Random(); //Seed to 0 for testing
	    float[][] noise = new float[width][height];
	 
	    for (int i = 0; i < width; i++)
	    {
	        for (int j = 0; j < height; j++)
	        {
	            noise[i][j] = (float)random.nextDouble() % 1;
	        }
	    }
	 
	    return noise;
	}
	
	public static float[][] GenerateSmoothNoise(float[][] baseNoise, int octave)
	{
	   int width = baseNoise.length;
	   int height = baseNoise[0].length;
	 
	   float[][] smoothNoise = new float[width][height];
	 
	   int samplePeriod = 1 << octave; // calculates 2 ^ k
	   float sampleFrequency = 1.0f / samplePeriod;
	 
	   for (int i = 0; i < width; i++)
	   {
	      //calculate the horizontal sampling indices
	      int sample_i0 = (i / samplePeriod) * samplePeriod;
	      int sample_i1 = (sample_i0 + samplePeriod) % width; //wrap around
	      float horizontal_blend = (i - sample_i0) * sampleFrequency;
	 
	      for (int j = 0; j < height; j++)
	      {
	         //calculate the vertical sampling indices
	         int sample_j0 = (j / samplePeriod) * samplePeriod;
	         int sample_j1 = (sample_j0 + samplePeriod) % height; //wrap around
	         float vertical_blend = (j - sample_j0) * sampleFrequency;
	 
	         //blend the top two corners
	         float top = Interpolate(baseNoise[sample_i0][sample_j0],
	            baseNoise[sample_i1][sample_j0], horizontal_blend);
	 
	         //blend the bottom two corners
	         float bottom = Interpolate(baseNoise[sample_i0][sample_j1],
	            baseNoise[sample_i1][sample_j1], horizontal_blend);
	 
	         //final blend
	         smoothNoise[i][j] = Interpolate(top, bottom, vertical_blend);
	      }
	   }
	 
	   return smoothNoise;
	}

	static float Interpolate(float x0, float x1, float alpha)
	{
	   return x0 * (1 - alpha) + alpha * x1;
	}
	
	public static float[][] GeneratePerlinNoise(float[][] baseNoise, int octaveCount)
	{
	   int width = baseNoise.length;
	   int height = baseNoise[0].length;
	 
	   float[][][] smoothNoise = new float[octaveCount][][]; //an array of 2D arrays containing
	 
	   float persistance = 0.5f;
	 
	   //generate smooth noise
	   for (int i = 0; i < octaveCount; i++)
	   {
	       smoothNoise[i] = GenerateSmoothNoise(baseNoise, i);
	   }
	 
	    float[][] perlinNoise = new float[width][height];
	    float amplitude = 1.0f;
	    float totalAmplitude = 0.0f;
	 
	    //blend noise together
	    for (int octave = octaveCount - 1; octave >= 0; octave--)
	    {
	       amplitude *= persistance;
	       totalAmplitude += amplitude;
	 
	       for (int i = 0; i < width; i++)
	       {
	          for (int j = 0; j < height; j++)
	          {
	             perlinNoise[i][j] += smoothNoise[octave][i][j] * amplitude;
	          }
	       }
	    }
	 
	   //normalisation
	   for (int i = 0; i < width; i++)
	   {
	      for (int j = 0; j < height; j++)
	      {
	         perlinNoise[i][j] /= totalAmplitude;
	      }
	   }
	 
	   return perlinNoise;
	}

}